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HCS-3DX, a next-generation AI-driven automated 3D-oid high-content screening system

Author

Listed:
  • Akos Diosdi

    (HUN-REN Biological Research Centre (HUN-REN BRC)
    Single-Cell Technologies Ltd
    University of Szeged)

  • Timea Toth

    (HUN-REN Biological Research Centre (HUN-REN BRC))

  • Maria Harmati

    (HUN-REN Biological Research Centre (HUN-REN BRC))

  • Grexa Istvan

    (HUN-REN Biological Research Centre (HUN-REN BRC))

  • Bálint Schrettner

    (HUN-REN Biological Research Centre (HUN-REN BRC))

  • Nora Hapek

    (HUN-REN Biological Research Centre (HUN-REN BRC))

  • Ferenc Kovacs

    (HUN-REN Biological Research Centre (HUN-REN BRC)
    Single-Cell Technologies Ltd)

  • Andras Kriston

    (HUN-REN Biological Research Centre (HUN-REN BRC)
    Single-Cell Technologies Ltd)

  • Krisztina Buzas

    (HUN-REN Biological Research Centre (HUN-REN BRC)
    University of Szeged)

  • Francesco Pampaloni

    (Goethe-Universität Frankfurt am Main)

  • Filippo Piccinini

    (IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”
    University of Bologna)

  • Peter Horvath

    (HUN-REN Biological Research Centre (HUN-REN BRC)
    Single-Cell Technologies Ltd
    Helmholtz Zentrum München
    University of Helsinki)

Abstract

Self-organised three-dimensional (3D) cell cultures, collectively called 3D-oids, include spheroids, organoids and other co-culture models. Systematic evaluation of these models forms a critical new generation of high-content screening (HCS) systems for patient-specific drug analysis and cancer research. However, the standardisation of working with 3D-oids remains challenging and lacks convincing implementation. This study develops and tests HCS-3DX, a next-generation system for HCS analysis in 3D imaging and image evaluation. HCS-3DX is based on three main components: an automated Artificial Intelligence (AI)-driven micromanipulator for 3D-oid selection, an HCS foil multiwell plate for optimised imaging, and image-based AI software for single-cell data analysis. We validated HCS-3DX directly on 3D tumour models, including tumour-stroma co-cultures. Our data demonstrate that HCS-3DX achieves a resolution that overcomes the limitations of current systems and reliably and effectively performs 3D HCS at the single-cell level. Its application will enhance the accuracy and efficiency of drug screening processes, support personalised medicine approaches, and facilitate more detailed investigations into cellular behaviour within 3D structures.

Suggested Citation

  • Akos Diosdi & Timea Toth & Maria Harmati & Grexa Istvan & Bálint Schrettner & Nora Hapek & Ferenc Kovacs & Andras Kriston & Krisztina Buzas & Francesco Pampaloni & Filippo Piccinini & Peter Horvath, 2025. "HCS-3DX, a next-generation AI-driven automated 3D-oid high-content screening system," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63955-5
    DOI: 10.1038/s41467-025-63955-5
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    References listed on IDEAS

    as
    1. David Ascheid & Magdalena Baumann & Jürgen Pinnecker & Mike Friedrich & Daniel Szi-Marton & Cornelia Medved & Maja Bundalo & Vanessa Ortmann & Asli Öztürk & Rajender Nandigama & Katherina Hemmen & Sül, 2024. "A vascularized breast cancer spheroid platform for the ranked evaluation of tumor microenvironment-targeted drugs by light sheet fluorescence microscopy," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    2. Gustavo Medeiros & Raphael Ortiz & Petr Strnad & Andrea Boni & Franziska Moos & Nicole Repina & Ludivine Challet Meylan & Francisca Maurer & Prisca Liberali, 2022. "Multiscale light-sheet organoid imaging framework," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
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